'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class        a__ (  UpperCAmelCase__				,  unittest.TestCase					):
     lowerCamelCase    :	Union[str, Any]				=KandinskyInpaintPipeline
     lowerCamelCase    :	List[Any]				=["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowerCamelCase    :	str				=[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowerCamelCase    :	List[Any]				=[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowerCamelCase    :	Tuple				=False






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         return 32






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         return 32






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         return self.time_input_dim






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.time_input_dim * 4






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return 1_00






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''       )
         return tokenizer






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         torch.manual_seed(0       )
         __lowerCamelCase            =    MCLIPConfig(
             numDims=self.cross_attention_dim    ,							transformerDimensions=self.text_embedder_hidden_size    ,							hidden_size=self.text_embedder_hidden_size    ,							intermediate_size=37    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							vocab_size=10_05    ,							)

         __lowerCamelCase            =    MultilingualCLIP(a       )
         __lowerCamelCase            =    text_encoder.eval()

         return text_encoder






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         torch.manual_seed(0       )

         __lowerCamelCase            =    {
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         __lowerCamelCase            =    UNetaDConditionModel(**a       )
         return model






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         torch.manual_seed(0       )
         __lowerCamelCase            =    VQModel(**self.dummy_movq_kwargs       )
         return model






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.dummy_text_encoder
         __lowerCamelCase            =    self.dummy_tokenizer
         __lowerCamelCase            =    self.dummy_unet
         __lowerCamelCase            =    self.dummy_movq

         __lowerCamelCase            =    DDIMScheduler(
             num_train_timesteps=10_00    ,							beta_schedule='''linear'''    ,							beta_start=0.0_00_85    ,							beta_end=0.0_12    ,							clip_sample=a    ,							set_alpha_to_one=a    ,							steps_offset=1    ,							prediction_type='''epsilon'''    ,							thresholding=a    ,							)

         __lowerCamelCase            =    {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						int    ,							a						:						Union[str, Any]=0       ):





         """simple docstring"""



         __lowerCamelCase            =    floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(a       )       ).to(a       )
         __lowerCamelCase            =    floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(seed + 1       )       ).to(a       )
         # create init_image
         __lowerCamelCase            =    floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(a       )       ).to(a       )
         __lowerCamelCase            =    image.cpu().permute(0    ,							2    ,							3    ,							1       )[0]
         __lowerCamelCase            =    Image.fromarray(np.uinta(a       )       ).convert('''RGB'''       ).resize((2_56, 2_56)       )
         # create mask
         __lowerCamelCase            =    np.ones((64, 64)    ,							dtype=np.floataa       )
         __lowerCamelCase            =    0

         if str(a       ).startswith('''mps'''       ):
             __lowerCamelCase            =    torch.manual_seed(a       )
         else:
             __lowerCamelCase            =    torch.Generator(device=a       ).manual_seed(a       )
         __lowerCamelCase            =    {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    '''cpu'''

         __lowerCamelCase            =    self.get_dummy_components()

         __lowerCamelCase            =    self.pipeline_class(**a       )
         __lowerCamelCase            =    pipe.to(a       )

         pipe.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    pipe(**self.get_dummy_inputs(a       )       )
         __lowerCamelCase            =    output.images

         __lowerCamelCase            =    pipe(
             **self.get_dummy_inputs(a       )    ,							return_dict=a    ,							)[0]

         __lowerCamelCase            =    image[0, -3:, -3:, -1]
         __lowerCamelCase            =    image_from_tuple[0, -3:, -3:, -1]

         print(f"""image.shape {image.shape}"""       )

         assert image.shape == (1, 64, 64, 3)

         __lowerCamelCase            =    np.array(
             [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]       )

         assert (
             np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




@slow
@require_torch_gpu
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''       )

         __lowerCamelCase            =    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
         __lowerCamelCase            =    np.ones((7_68, 7_68)    ,							dtype=np.floataa       )
         __lowerCamelCase            =    0

         __lowerCamelCase            =    '''a hat'''

         __lowerCamelCase            =    KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''    ,							torch_dtype=torch.floataa       )
         pipe_prior.to(a       )

         __lowerCamelCase            =    KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''    ,							torch_dtype=torch.floataa       )
         __lowerCamelCase            =    pipeline.to(a       )
         pipeline.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    torch.Generator(device='''cpu'''       ).manual_seed(0       )
         __lowerCamelCase      ,    __lowerCamelCase            =    pipe_prior(
             a    ,							generator=a    ,							num_inference_steps=5    ,							negative_prompt=''''''    ,							).to_tuple()

         __lowerCamelCase            =    pipeline(
             a    ,							image=a    ,							mask_image=a    ,							image_embeds=a    ,							negative_image_embeds=a    ,							generator=a    ,							num_inference_steps=1_00    ,							height=7_68    ,							width=7_68    ,							output_type='''np'''    ,							)

         __lowerCamelCase            =    output.images[0]

         assert image.shape == (7_68, 7_68, 3)

         assert_mean_pixel_difference(a    ,							a       )

'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
